Preamble¶
This notebook deals with compiling all available estimates and measurements of vertical nitrate fluxes (in the Arctic Ocean).
All figures exported from this notebook are prefixed by FIGURE_NO3-COMP_.
In [1]:
%load_ext autoreload
%autoreload 2
%run imports.py
dims = dict(
FN = hv.Dimension('FN', label='Nitrate flux', unit='mmol N m⁻² d⁻¹', range=(.005, 10)),
no3_sfc_winter_uM = hv.Dimension('no3_sfc_winter_uM', label='Pre-bloom sfc. NO₃ ', unit='µM', range=(-.5, 13)),
strat=hv.Dimension('strat', label='Brunt-Väisälä frequency', unit='s⁻²')
)
Maps¶
In [16]:
# df = gpd.read_file('../data/fn-compilation-database/fn-compilation.shp')
# df = gpd.read_file('../data/fn-compilation')
df = pd.read_csv('../data/fn-compilation.csv')
# df['geometry'] = df.centroid
# df.Longitude = df.geometry.x
# df.Latitude = df.geometry.y
# df = pd.DataFrame(df.drop(columns='geometry'))
# df['FN'] = df.FN.replace(0, 1e-10)
options_bk = [
opts.Points(
backend='bokeh',
color=dim('FN'),
clipping_colors={'min': 'grey', 'NaN': 'grey'},
#hooks=[logcolor_ticks([1e-2, 1e-1, 1, 5])],
tools=['hover'], cmap=default_cmap, colorbar=True,
size=dim('samplesize').categorize({'single': 9, 'aggregate': 20}),
line_color='k',
width=500, height=400, show_legend=False),
opts.Labels(xoffset=5e3, yoffset=5e3),
opts.Feature(scale='50m'),
]
options_mpl = translate_options(options_bk, bokeh2mpl)
options = options_mpl + options_bk
data = gv.Points(df,
kdims=['Longitude', 'Latitude'],
vdims=['FN','samplesize', 'Reference', 'Season'],
crs=ccrs.PlateCarree()
)
# coastline = gf.coastline.clone(extents=(-180, 70, 180, 90))
l = land * data #* gv.Labels(data)
# l = data
l = l.redim.range(Latitude=(60,90), Longitude=(-180, 180))
ll = (
l
.opts(*(opts_map+options), clone=True)
.opts(
opts.Points(
backend='bokeh',
color=np.log10(dim('FN')).clip(min=-2), hooks=[logcolor_ticks([1e-2, 1e-1, 1, 5])]
)
)
)
hv.renderer('bokeh').theme = theme
hv.save(ll, '../nb_fig/FIGURE_FN-COMP_map.html')
ll
Out[16]:
In [3]:
def finalize(fig):
ax = fig.axes[0]
ax.background_img()
fig = mplrender_map(
l
.opts(
*opts_map_mpl, *options_mpl,
opts.Points(
logz=True,
cmap=cmocean.cm.solar, zlim=(1e-2, 5), clipping_colors={'min': 'grey', 'NaN': 'grey'},
backend='matplotlib'
),
opts.Feature(scale='10m'),
clone=True
),
'../nb_fig/FIGURE_FN-COMP_map',
hooks=[finalize],
)
fig
Out[3]:
GridMatrix (faceted)¶
Seasonal cycle¶
In [4]:
df = pd.read_csv('../data/fn-compilation.csv')
df_nonwrap = df.loc[df.validity_start<df.validity_end]
df_wrap = df.loc[df.validity_start>df.validity_end]
df = pd.concat([
df_nonwrap,
df_wrap.assign(measured_end=13, validity_end=13),
df_wrap.assign(measured_start=1, validity_start=1)
])
# reshape the start and end times of the different periods into a tidy tabular format
# for easier ingestion in holoviews
def melt_df(x):
dfr = df.melt(id_vars=['Reference', 'FN', 'samplesize', 'overturning'], value_vars=['measured_'+x, 'validity_'+x],
value_name=x, var_name='type')
dfr.loc[dfr.type.str.contains('measured'), 'type']='measured'
dfr.loc[dfr.type.str.contains('valid'), 'type']='valid'
return dfr
df = melt_df('start').join(melt_df('end')[['end']])
df = df.replace(dict(
aggregate='Larger-scale aggregate', single='Few stations', overturning='Winter overturning', perennial='Perennial stratification',
measured='Measured', valid='Stipulated'
))
df.loc[df.start==df.end, 'end'] += 1
df.head()
Out[4]:
In [5]:
t=pd.date_range('2000-1-1', '2001-1-1', freq='2MS')
xticks = list(zip(t.month+.5, t.month_name().str[:3]))
od = {
'overturning': {'color': ['orange', 'blue']},
'type': {'line_width': [6, 3]},
'samplesize': {'alpha': [.3, 1]}
}
options = (
opts.NdOverlay(legend_position='right', show_legend=False, width=800),
opts.Segments(logy=True, padding=.1, xticks=xticks, show_title=False, title_format='',
toolbar=None, yformatter='%g', **faceted_legend_opts(od), xlabel=''),
)
l = hv.Dataset(df.assign(fn2=df.FN), ['start', 'FN', 'end', 'fn2'] + ['overturning', 'type', 'samplesize']).to(hvu.Segments).overlay()
l = l.redim(**dims)
l = l.opts(*options) + faceted_legend(l, od, hw={'height':200, 'width':250})
fname = '../nb_fig/FIGURE_FN-COMP_chart_seasonal_cycle'
ll = l.opts(toolbar=None, clone=True)
hv.save(ll, fname, fmt='png')
save_bokeh_svg_multipanel(ll, fname+'.svg')
hv.save(l, fname, fmt='html')
l
Out[5]:
Surface nitratre concentration vs. stratification vs. bathymetry¶
FN- winter NO3¶
In [6]:
df = pd.read_csv('../data/fn-compilation.csv')
df.overturning = df.overturning.str.capitalize()
options = [
opts.Scatter(
color='black',
marker=hv.Cycle(['triangle', 'square']),
show_grid=False,
legend_position='top_left', show_legend=True,
height=350, width=350, #color=dim('label'),
size=10,
logx=True, xformatter='%g'),
]
l = (
hv.Dataset(df, ['FN', 'overturning'], 'no3_sfc_winter_uM')
.to(hv.Scatter).overlay('overturning')
.redim(**dims)
)
l = l.opts(*options).redim.range(FN=((5e-3,6))).relabel('A')
hv.renderer('bokeh').theme = theme
fname = '../nb_fig/FIGURE_FN-COMP_chart_vs_NO3_panelA'
hv.save(l.opts(toolbar=None, clone=True), fname, fmt='html')
hv.save(l.opts(toolbar=None, clone=True), fname, fmt='png')
save_bokeh_svg(l, fname+'.svg')
panel_A = l.clone()
panel_A
Out[6]:
Panel B: Surface nitratre vs. stratification grouped by bathymetry¶
In [7]:
unicode_dict = {i:j for i, j in zip(range(10), list('⁰¹²³⁴⁵⁶⁷⁸⁹'))}
def format_exponent_unicode(n):
if n>0:
return '10'+unicode_dict[n]
elif n<0:
return '10⁻'+unicode_dict[abs(n)]
In [8]:
df = (
pd.read_csv('../data/no3-compilation/database-per-stn.csv', parse_dates=['date'])
.rename(columns=dict(N2_30_100='strat'))
)
df = df.assign(logstrat=np.log10(df.strat))
df = df.loc[df.month.between(3, 5)]
ds = xr.load_dataarray('/Users/doppler/database/IBCAO/IBCAO_1min_bathy.nc')
df['btm'] = - ds.sel(lat=df.lat.values, lon=df.lon.values, method='nearest').values.diagonal()
# df = df.groupby('reg_name', as_index=False).mean()
df = df.dropna(subset=['reg_name'])
df['btm_cat'] = pd.cut(df.btm, bins=[0, 200, 1500, np.inf], labels=['Shelf', 'Slope', 'Basin'])
strat_bins = np.arange(-6, -3, .5)
df['strat_cat'] = pd.cut(df.logstrat, bins=strat_bins)
btm_cats = sc.data.keys()
# ds = hv.Dataset('logstrat', ['ntr0', 'btm_cat'])
def binavg(el):
el = bin_average(el, bins=strat_bins, avg_fun=np.nanmean)
return el.to(hv.Curve).clone(data=el.dframe().dropna())# * el
# colors = hv.Cycle(colorcet.b_glasbey_hv)
colors = hv.Cycle(colorcet.b_glasbey_category10)
# colors = hv.Cycle(['#282E3E', '#3F8196', '#41E2DD'])
# colors = hv.Cycle(['#2E6353', '#D1C461', '#E78A9C'])
options = [
opts.Scatter(color=colors, tools=['hover']),
opts.Curve(
color=colors, line_width=5, height=350, width=500, tools=['hover'],
xticks=[(logx, format_exponent_unicode(logx)) for logx in range(-6, -2)],
xlim=(-6,-3.5),
ylabel='',
),
opts.RGB(),
opts.Contours(
show_legend=False,
cmap=hv.Cycle([[c] for c in colors.values]),
alpha=dim('Density').norm(),
line_dash='', line_width=3*np.sqrt(dim('Density').norm()),
),
opts.Overlay(legend_position='bottom_left'),
]
avg = hv.NdOverlay({x: binavg(sc[x]) for x in btm_cats})
kde_args = dict(
# levels=np.arange(0.01, 0.5, 0.02),
bandwidth=0.3
)
kde = hv.NdOverlay({var: bivariate_kde(sc[var], **kde_args) for var in btm_cats})
l = kde * avg
l = l.opts(*options).redim(ntr0='no3_sfc_winter_uM', logstrat='strat').redim(**dims)
panel_B = l.clone().relabel('B')
panel_B
The overall statistical significance is hard to assess, but a category-wise box/whisker plot also consistently shows the ordering Basin < Shelf < Slope:
In [ ]:
hv.BoxWhisker(df, ['strat_cat', 'btm_cat'], 'ntr0').opts(xrotation=45, width=1000)
Merge panels¶
In [ ]:
l = panel_A + panel_B
fname = '../nb_fig/FIGURE_FN-COMP_chart_vs_NO3'
hv.save(l.opts(toolbar=None, clone=True), fname+'.html')
save_bokeh_svg_multipanel(l, fname+'.svg')
os.system(f'convert -density 200 {fname}.svg {fname}.png')